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Lane detection critical to alert driver to avert car departure from driving lanes is an important issue in Intelligent Vehicle Safety System. Traditional lane detection uses straight line detection approaches like Canny and Hough Line Transforms to detect driving lanes, failing to detect curvy lanes. To solve curvy lane detection and to speed up real-time performance, we propose an Android-based solution for lane detection and departure warning. To achieve straight and cure lane detection, we use adaptive threshold algorithm, frequency of lane appearance and mathematical function to design color-based algorithm. With the camera aligned to the direction of car driving, the middle line of the onboard image is used to check lane departure warning. To speed up real-timer performance, image quality is down-sampled before it is split in half for multi-thread processing by the multi-core CPU commonly available on Android platforms. In contrast to traditional approaches, our solution, solving curvy lane detection with a fps performance roughly doubled, shows much improvement to existing lane detection techniques.

This paper proposes an extension of the surveillance camera's function as an intelligent attendance logging system. The system works as a time recorder within two phases; learning phase and monitoring phase. By placing a camera inside a working room, the sytem enters the learning phase by locating the sitting area automatically based on the camera's images. The learning phase has a defined time duration to complete a working map. Next, the system switches to the monitoring phase to report each occupant's working hour corresponding to their sitting area in the working map. The system detects the entering occupant, the leaving occupant, the sitting occupant, and the standing occupant from their seat. The system also tracks the occupants. The working hours of the occupant will be counted as how long they sit in their desk. When the occupant sits at his/her seat, a start time is given. Later, after he/she leaves from their seat, a stop time is generated. The experimental setup has been done in our laboratory. The result shows that the system can achieve a good result.

Combining depth information and color image, D-RGB cameras provide a ready detection of human and associated 3D skeleton joints data, facilitating, if not revolutionizing, conventional image centric researches in, among others, computer vision, surveillance, and human activity analysis. Applicability of a D-RBG camera, however, is restricted by its limited range of frustum of depth in the range of 0.8 to 4 meters. Although a D-RGB camera network, constructed by deployment of several D-RGB cameras at various locations, could extend the range of coverage, it requires precise localization of the camera network: relative location and orientation of neighboring cameras. By introducing a skeleton-based viewpoint invariant transformation (SVIT), which derives the relative location and orientation of a detected humans upper torso to a D-RGB camera, this paper presents a reliable automatic localization technique without the need for additional instrument or human intervention. By respectively applying SVIT to two neighboring D-RGB cameras on a commonly observed skeleton, the respective relative position and orientation of the detected humans skeleton for these two cameras can be obtained before being combined to yield the relative position and orientation of these two cameras, thus solving the localization problem. Experiments have been conducted in which two Kinects are situated with bearing differences of about 45 degrees and 90 degrees; the coverage can be extended by up to 70% with the installment of an additional Kinect. The same localization technique can be applied repeatedly to a larger number of D-RGB cameras, thus extending the applicability of D-RGB cameras to camera networks in making human behavior analysis and context-aware service in a larger surveillance area.